Improving flight delays prediction by developing attention-based bidirectional LSTM network

Mamdouh, Maged; Ezzat, Mostafa; Hefny, Hesham

doi:10.1016/j.eswa.2023.121747

Cited by 7 publications

References 68 publications

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Sustainability with Limited Data: A Novel Predictive Analytics Approach for Forecasting CO2 Emissions

Filelis-Papadopoulos,

Kirshner,

O’Reilly

2024

Inf Syst Front

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Unforeseen events (e.g., COVID-19, the Russia-Ukraine conflict) create significant challenges for accurately predicting CO2 emissions in the airline industry. These events severely disrupt air travel by grounding planes and creating unpredictable, ad hoc flight schedules. This leads to many missing data points and data quality issues in the emission datasets, hampering accurate prediction. To address this issue, we develop a predictive analytics method to forecast CO2 emissions using a unique dataset of monthly emissions from 29,707 aircraft. Our approach outperforms prominent machine learning techniques in both accuracy and computational time. This paper contributes to theoretical knowledge in three ways: 1) advancing predictive analytics theory, 2) illustrating the organisational benefits of using analytics for decision-making, and 3) contributing to the growing focus on aviation in information systems literature. From a practical standpoint, our industry partner adopted our forecasting approach under an evaluation licence into their client-facing CO2 emissions platform.

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